{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网络添加了Instance Normalize\n",
    "任务一：\n",
    "输入1（cycle）2（EMG_retus_femoris_l=emg_rf_l），输出4（muscleForce_retus_femoris_l=mf_rf_l）\n",
    "\n",
    "似乎把2做整体输入时，cycle可以省略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import random\n",
    "import scipy.io as scio\n",
    "import numpy as np\n",
    "from visdom import Visdom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import torch.nn.init as init\n",
    "from torchsummary import summary\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import datetime\n",
    "#import tool\n",
    "from tool import *\n",
    "import torch.nn.functional as F\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
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      "Setting up a new session...\n",
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      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n",
      "Setting up a new session...\n"
     ]
    }
   ],
   "source": [
    "#文件路径\n",
    "timeForSave = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n",
    "#path = 'G:\\科研学习\\肌电信号\\Code\\musle force\\\\'\n",
    "path ='/mnt/data/ZP_Project/Muscle/muscle/'\n",
    "Visdom_Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Visdom\\\\'\n",
    "if not os.path.exists(Visdom_Dir):\n",
    "    os.makedirs(Visdom_Dir)\n",
    "image_Dir =path+'image_Mission1_FC_IN_all_together'\n",
    "if not os.path.exists(image_Dir):\n",
    "        os.makedirs(image_Dir)\n",
    "\n",
    "\n",
    "# 可视化\n",
    "class visdom_account:\n",
    "    def __init__(self):\n",
    "        self.port = 8097\n",
    "        self.server = \"http://localhost\"\n",
    "        self.base_url = \"/\"\n",
    "        self.username = \"admin\"\n",
    "        self.passward = \"1234\"\n",
    "        self.evns = \"Muscle\"\n",
    "\n",
    "\n",
    "viz_acnt = visdom_account()\n",
    "\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz.delete_env('Muscle')  # 删除环境\n",
    "\n",
    "# viz.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#          opts={'title': 'ProcessMonitor', },)\n",
    "# viz1.text('MONITOR: Show train process~~', win='Monitor',\n",
    "#           opts={'title': 'ProcessMonitor', },)\n",
    "viz = Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave)\n",
    "viz1 = Visdom(env=viz_acnt.evns,\n",
    "              log_to_filename=Visdom_Dir+'vislog_'+timeForSave)\n",
    "viz_batch = []\n",
    "for i in range(32):\n",
    "    viz_batch.append(Visdom(env=viz_acnt.evns, log_to_filename=Visdom_Dir +\n",
    "             'vislog_'+timeForSave))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed_redacted_GIL03_XSlow1.mat\n",
      "Transformed_redacted_GIL12_Fast2.mat\n",
      "Transformed_redacted_GIL02_Free2.mat\n",
      "Transformed_redacted_GIL12_slow4.mat\n",
      "Transformed_redacted_GIL04_slow3.mat\n",
      "Transformed_redacted_GIL04_Fast2.mat\n",
      "Transformed_redacted_GIL02_Fast3.mat\n",
      "Transformed_redacted_GIL04_Free3.mat\n",
      "Transformed_redacted_GIL06_slow2.mat\n",
      "Transformed_redacted_GIL11_Fast2.mat\n",
      "Transformed_redacted_GIL08_Fast1.mat\n",
      "Transformed_redacted_GIL03_Free4.mat\n",
      "Transformed_redacted_GIL06_Fast3.mat\n",
      "Transformed_redacted_GIL08_slow4.mat\n",
      "Transformed_redacted_GIL03_xFast2.mat\n",
      "Transformed_redacted_GIL01_Fast5.mat\n",
      "Transformed_redacted_GIL11_XSlow1.mat\n",
      "Transformed_redacted_GIL08_XSlow2.mat\n",
      "Transformed_redacted_GIL06_XSlow1.mat\n",
      "Transformed_redacted_GIL11_Free1.mat\n",
      "Transformed_redacted_GIL12_Free4.mat\n",
      "Transformed_redacted_GIL01_XSlow2.mat\n",
      "Transformed_redacted_GIL03_slow3.mat\n",
      "Transformed_redacted_GIL11_slow2.mat\n",
      "Transformed_redacted_GIL06_Free2.mat\n",
      "Transformed_redacted_GIL01_Free4.mat\n",
      "Transformed_redacted_GIL01_slow3.mat\n",
      "Transformed_redacted_GIL04_XSlow1.mat\n",
      "Transformed_redacted_GIL02_slow1.mat\n",
      "Transformed_redacted_GIL08_Free1.mat\n",
      "Transformed_redacted_GIL02_XSlow2.mat\n",
      "Transformed_redacted_GIL12_XSlow1.mat\n"
     ]
    }
   ],
   "source": [
    "#读取并合并\n",
    "#尝试直接把数据相加\n",
    "Dir = 'Transed_Data'\n",
    "filenames = os.listdir(path+Dir)\n",
    "# Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\\\'\n",
    "# filenames = os.listdir('G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data')\n",
    "\n",
    "data_emg = []\n",
    "data_mf = []\n",
    "#data_kal = []\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    data = scio.loadmat(path+Dir +'//'+ str(filename))\n",
    "    #print(data.keys())  #dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n",
    "    data1 = data['emg_rf_l']\n",
    "    #data1 = data1.squeeze()\n",
    "    data2 = data['mf_rf_l']\n",
    "    #data3 =  data['ka_l']\n",
    "    #data2 = data2.squeeze()\n",
    "    data_emg.append(data1)\n",
    "    data_mf.append(data2)\n",
    "    #data_kal.append(data3)\n",
    "\n",
    "data_emg = np.array(data_emg)\n",
    "data_mf = np.array(data_mf)\n",
    "#data_kal = np.array(data_kal)\n",
    "set=MuscleData1(data_emg,data_mf)\n",
    "train_set,test_set = torch.utils.data.random_split(set, [28, 4])\n",
    "    # sampel = next(iter(set))\n",
    "    # print(sampel,'\\nnnnnnn')\n",
    "\n",
    "# train_dataset, test_dataset = torch.utils.data.random_split(set,[28,4])\n",
    "\n",
    "# train_loader =torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True, pin_memory=False)\n",
    "# test_loader =torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=True, pin_memory=False)\n",
    "# train_test_loader = torch.utils.data.DataLoader(set, batch_size=1, shuffle=True, pin_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_27371/508928774.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     46\u001b[0m                 \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0;31m#viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m                 \u001b[0mviz_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvizx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'append'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'train_loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m                 \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m                 \u001b[0;31m#n +=1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36mline\u001b[0;34m(self, Y, X, win, env, opts, update, name)\u001b[0m\n\u001b[1;32m   1712\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1714\u001b[0;31m         return self.scatter(X=linedata, Y=labels, opts=opts, win=win, env=env,\n\u001b[0m\u001b[1;32m   1715\u001b[0m                             update=update, name=name)\n\u001b[1;32m   1716\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, X, Y, win, env, opts, update, name)\u001b[0m\n\u001b[1;32m   1638\u001b[0m             \u001b[0mendpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'update'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1640\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_send\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1642\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mpytorch_wrap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36m_send\u001b[0;34m(self, msg, endpoint, quiet, from_log, create)\u001b[0m\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m             return self._handle_post(\n\u001b[0m\u001b[1;32m    709\u001b[0m                 \"{0}:{1}{2}/{3}\".format(self.server, self.port,\n\u001b[1;32m    710\u001b[0m                                         self.base_url, endpoint),\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/visdom/__init__.py\u001b[0m in \u001b[0;36m_handle_post\u001b[0;34m(self, url, data)\u001b[0m\n\u001b[1;32m    675\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \"\"\"\n\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'POST'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    540\u001b[0m         }\n\u001b[1;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    443\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    438\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1346\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1349\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ZP/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"此代码块为Net训练\"\"\"\n",
    "\n",
    "Net = FC_IN_Net()\n",
    "Net.apply(weights_init)\n",
    "Net.to(device)\n",
    "\n",
    "train_loader=torch.utils.data.DataLoader(train_set, batch_size=4, shuffle=True, pin_memory=False)\n",
    "test_loader=torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=True, pin_memory=False)\n",
    "\n",
    "\n",
    "criterion = torch.nn.MSELoss()\n",
    "epoch_num = 10000\n",
    "\n",
    "optimizer = torch.optim.SGD(Net.parameters(),\n",
    "lr=0.0000015,momentum=0.9)\n",
    "vizx = 0\n",
    "#开始训练\n",
    "for epoch in range(epoch_num):\n",
    "        #n = 0   #此n用于visdom画图。每个epoch对每个样本进行画图\n",
    "        for batch in train_loader:\n",
    "                vizx+=1\n",
    "                data,label = batch\n",
    "                data = torch.as_tensor(data)\n",
    "                #data2 = torch.as_tensor(data2)\n",
    "                data = data.type(torch.FloatTensor)\n",
    "                #data2 = data2.type(torch.FloatTensor)\n",
    "                \n",
    "                #print(data.shape)       #torch.Size([1, 101, 1])\n",
    "                label =torch.as_tensor(label)\n",
    "                label = label.squeeze(2)\n",
    "                #print(label.shape)\n",
    "                label = label.type(torch.FloatTensor)\n",
    "                data = data.cuda()\n",
    "                #data2 = data2.cuda()\n",
    "                label = label.cuda()\n",
    "                #print(data.shape)\n",
    "                pred = Net(data)\n",
    "                #print(pred.shape)\n",
    "                #print(label.shape)\n",
    "                #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "                #print('pred',pred.shape)\n",
    "                #print('label',label.shape)     #label torch.Size([101, 1])\n",
    "                loss = criterion(pred,label)\n",
    "                \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                #viz.line([float(loss)],[vizx],win='loss', name='loss-batch', update='append')\n",
    "                viz_batch[0].line([float(loss)],[vizx],win='loss'+str(0), name='loss'+str(0), update='append',opts=dict(title='train_loss'))\n",
    "                optimizer.step()\n",
    "                #n +=1\n",
    "                #print(loss)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i =0\n",
    "#测试\n",
    "for batch in test_loader:\n",
    "        data1,label = batch\n",
    "        data1 = torch.as_tensor(data1)\n",
    "        #data2 = torch.as_tensor(data2)\n",
    "        data1 = data1.type(torch.FloatTensor)\n",
    "        #data2 = data2.type(torch.FloatTensor)\n",
    "        data1 = data1.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        #data2 = data2.cuda() #data1用于输入网络，类型为tensor    data用于画图，类型为np。因为不需要对data的数值进行改动所以进行了浅拷贝\n",
    "        # label =label.to(device)\n",
    "        #data1 = data1.reshape(-1,1,101)  #CNN需要,FC时注释掉\n",
    "        pred = Net(data1)\n",
    "        #pred = pred.reshape(-1,101,1)    #CNN徐，FC时注释掉\n",
    "        pred = pred.cpu()\n",
    "        pred = pred.data.numpy()\n",
    "        pred=np.squeeze(pred)\n",
    "        label=np.squeeze(label)\n",
    "        data1 =data1.cpu()\n",
    "        #data2 =data2.cpu()\n",
    "        data1 = np.squeeze(data1)\n",
    "        #data2 = np.squeeze(data2)\n",
    "        #利用plot画图\n",
    "        plt.figure(i)\n",
    "        plt.title('test_sample'+str(i))\n",
    "        plt.plot(pred, label='pred')\n",
    "        plt.plot(label, color='red', label='GroundTruth')\n",
    "        plt.plot(data1*100, color='black', label='data1')\n",
    "        #plt.plot(data2*100, color='black', label='data2')\n",
    "        plt.legend()\n",
    "        #增\n",
    "        figname =image_Dir+'/imag_ {}.jpg'.format(i)\n",
    "        plt.savefig(figname)\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "        i +=1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #用于打印网络里权重。默认注释掉\n",
    "# Net.state_dict().keys()\n",
    "# for i in range(len(test_loader)):\n",
    "#     print(i)\n",
    "#     # print(Net[i].fc1.weight)\n",
    "#     # print(Net[i].fc2.weight)\n",
    "#     # print(Net[i].out.weight)\n",
    "#     w1 =Net[i].fc1.weight.cpu().data.numpy()\n",
    "#     w2 =Net[i].fc2.weight.cpu().data.numpy()\n",
    "#     w3 =Net[i].out.weight.cpu().data.numpy()\n",
    "#     w1 = np.squeeze(w1)\n",
    "#     w2 = np.squeeze(w1)\n",
    "#     w3 = np.squeeze(w1)\n",
    "#     #ss\n",
    "#     plt.figure(i)\n",
    "#     plt.title('Net'+str(i))\n",
    "#     plt.plot(w1, label='fc1.weight')\n",
    "#     plt.plot(w2, color='red', label='fc2.weight')\n",
    "#     plt.plot(w3, color='black', label='out.weight')\n",
    "#     plt.legend()\n",
    "#     #figname ='./image_Mission1_FC/imag_ {}.jpg'.format(i)\n",
    "#     #plt.savefig(figname)\n",
    "#     plt.show()\n",
    "#     plt.close()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e3b95d8d6e207246d09e19daee9bbc5bbe4502ff924268508716ea3969933742"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('pytorch')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
